Taller en Análisis filogenéticos comparativos en Ecofisiología

Aplicación de Mesquite y R

Programa

• Primero (11 Diciembre)• Introduction to Mesquite and R• Data Preparation and Manipulation• Tardes -

– Practical Use of Mesquite y R

• Segundo (12 Diciembre)• Selection of Phylogenetic Trees• Supertrees – Assemblying Composite Trees

– Sources of Phylogenetic Hypotheses• Estimation of Ancestral Character States

– Categorical (Mesquite & R)– Continuous (R – ace)

Programa

• Tercera (13 Diciembre)– Estimation of Phylogenetic Signal– Statistical Methods incorporating Phylogenetic

information• Phylogenetic Independent Contrasts• Phylogenetic GLS• Multivariate Analyses

– Bring your own Data!

Goals of Comparative Analyses

• Investigar la evolución carácter• La coevolución de caracteres• Control de la no independencia de las especies• Hipótesis de ensayo de adaptación

Estadísticas tradicionales de asumir la independencia de las especies (unidades de muestreo)…

Pero, las especies exhiben diferentes niveles de relación, que afecta a las inferencias de la adaptación

local y la diversificación

Pearman et al. TREE 2008

Estimación carácter ancestral

Garganta Morphs en UrosaurusFeldman et al. 2011. Molecular Phylogenetics and Evolution

Dos importantes programas

http://mesquiteproject.org/mesquite/mesquite.html

http://cran.r-project.org/

Objetivos de Mesquite

• Manipulate Phylogenetic Trees– Estimate Ancestral Character States– Estimate Character Correlations– Inferences of Character Evolution– Multivariate Analyses

Objetivos para

• How to use R to Manipulate Data• Phylogenetic Comparative Analysis• Statistical Analyses not available in Mesquite

Ventajas de

• Free• Many packages available• Powerful and Flexible• Platform Independent– MacOS– Linux– Windows

Página de Inicio para R

Console de

R Studio – A GUI for

http://www.rstudio.com/

37 Paquetes filogenéticos en

• ape• caper• geiger• motmot• OUwie• phylobase• phyloclim• phytools• picante

Sólo voy a describir estos paquetes

Datos Necesarios

• Phylogenetic Tree– NEXUS format – NEWICK format ((B:0.2,(C:0.3,D:0.4)E:0.5)F:0.1)A

• Data– Continuous– Discrete– Flat Format (Texto, ASCII)

Nexus Data File Format#nexus...begin trees; translate 1 Phrynosoma, 2 Uta, 3 Petrosaurus, 4 Urosaurus, 5 Sceloporus ; tree one = [&U] (1,2,(3,(4,5)); tree two = [&U] (1,3,(5,(2,4));end;

A tutorial in Mesquite• Tres elementos de Mesquite

1. Characters2. Taxa3. Trees

Primero Ventana de Mesquite

Log – list of commands

Projects and Files – list of open projects

Crear un nuevo proyecto (archivo)

Nuevas opciones de archivo

Numero de caracteres

y el tipo de caracteres

Ventana de caracteres

Podemos anotar caracteres“Show Annotations Panel”

MetaData

Taxa se pueden asignar a grupos

Los árboles pueden establecerse en diferentes formas

Taxa ventana

Select a Tree

Ver el árbol

Total del Proyecto

A Gentle Introduction to

El intérprete interactivo, R

Asigna variables

Types of Variables

Mode ExampleNumeric 10.2, 20Character “Morph”, “Substrate”Factor CategoricalLogical TRUE, FALSE, T, F

Operators

Functions

Combinando elementos en matrices

Matrices

Operations on Matrices

Dataframes

• Rectangular table of information– Can include numbers, text

• This is the form of your data when you import into R

Character 1 Character 2 Character 3 Character 4 Character 5

Species 1

Species 2

Species 3

Character1 Character2 Character3 Character4 Character5

Species1 22.1 100.3 15 2.2 22Species2 23.7 125.0 17.6 3.8 25Species3 35.2 98.3 22.1 1.9 19

Morphology =

Dataframe Behaves as a Matrix

Use attach to directly refer to variable names

attach(morphology)

Character 2 # gives all values of character 2

No Spaces in species or variable names

Use Help

Importación de datos

• Change the “working directory”– Easy in R Studio

• Data should be in a clean rectangular matrix– Flat File (No formatting), ASCII text– Exported from excel

• First row: Variable Names• First column: Species/Taxon Names• example: Iguana Life History Data

Species SVL Mass CS RCM EggM EggS EggV OffSVL AdS AgeMat Env Amblyrhynchus_cristatus 279.0 1370.0 2.6 0.18 98.6 90.33 21.8 NA 0.85 41.0 IslandConolophus_pallidus 440.0 4300.0 10.0 NA NA NA NA NA NA NA IslandConolophus_subcristatus 415.0 3600.0 13.5 0.199 51.2 63.4 NA NA 0.9 84.0 IslandCtenosaura_clarki 126.58 70.78 8.5 0.24 2.45 23.37 3.05 NA NA NA MainCtenosaura_hemilopha 219.33 375.0 27.33 0.21 2.37 21.22 2.69 NA NA NA MainCtenosaura_pectinata 238.7 482.0 28.0 0.23 3.92 26.3 2.29 NA NA NA MainCtenosaura_similis 238.39 795.13 31.1 0.4 7.72 30.92 2.28 NA 0.78 22.0 MainCyclura_carinata 225.0 605.3 5.1 0.21 25.0 52.0 44.87 NA 0.9 72.0 IslandCyclura_ricordi 355.0 1275.0 10.2 NA NA NA NA NA NA NA IslandCyclura_cychlura 405.0 2805.0 8.75 0.21 68.69 73.01 61.34 96.0 NA NA IslandCyclura_nubila 340.0 1700.0 8.12 NA NA NA NA 99.8 NA NA IslandCyclura_cornuta 355.0 3745.6 15.76 NA NA NA NA NA 0.9 72.0 IslandCyclura_inornata 320.0 1336.0 4.1 0.165 55.12 66.0 NA 95.0 NA 132.0 IslandCyclura_stejnegeri 475.0 4516.0 2.4 0.06 115.0 81.66 122.45 NA NA 110.0 IslandDipsosaurus_dorsalis 123.0 70.0 5.6 NA NA NA NA NA 0.66 32.0 MainIguana_iguana 360.35 115.65 32.86 0.46 15.7 39.35 NA NA NA NA MainSauromalus_obesus 160.55 180.0 8.59 0.38 8.0 25.0 15.0 NA 0.8 48.0 MainSauromalus_hispidus 279.0 900.0 22.2 0.24 10.0 25.0 24.0 NA NA NA IslandSauromalus_varius 293.6 1200.0 23.4 0.35 18.0 40.0 28.0 NA NA NA IslandCrotaphytus_collaris 84.8 24.66 8.6 0.217 1.23 21.3 NA NA 0.48 12.0 Main

Importing Data• Workhorse function: read.table()

iguana.lh <- read.table(file=“iguanalh.txt”, header=TRUE)• iguana.lh (dataframe name)• Check to make sure data were read in correctly• iguana.lh[1:10,] # look at first 10 rows

Otras formas de importar datos

• Other formats: read.csv(), read.delim()• (useful if there are spaces within some fields)• Handy function: file.choose() # navigate to file

iguana.lh <- read.table(file=file.choose(), header=T)

attach(iguana.lh) # easy to manipulate variables

Factors• Used to represent categorical data; by default, read.table()

• converts columns with characters into factors

• Factors look like strings, but are treated differently by functions• species # example of a factor• Factors have levels, which are the unique values it takes

• levels(species) # example of a factor• Factor levels may be ordered (e.g., low, med, high), which is

important in some analyses (see ?factor and ?ordered)

Faltan Datos• Represented by a special character in R: NA• Many functions have an argument na.rm

– If TRUE, NA’s are removed– FALSE is usually default (function returns NA)– median(x, na.rm=TRUE)

• read.table(file, na.strings=“NA”)• na.strings=“-999” # here, missing data are -999• • Useful function: complete.cases(iguanlh.txt)• • Returns logical vector, T for rows without missing data• cc<- complete.cases(iguanalh.txt)

Los métodos filogenéticos en R

• Start with a Tree• library(ape) # load ape package• We can create a random tree:• tree <- rtree(25) # 25 terminal taxa

• Normally, read in tree(s) from file• tree <- read.nexus(file) # Nexus format• tree <- read.tree(file, …) # Newick format

Tree Structure in ape

Plot a Tree

View a Tree

Manana

Tree SelectionAncestor Character State Estimation